POS0231 GENETIC BIOMARKERS, SNP GENES AND mtDNA HAPLOGROUPS, PREDICT OSTEOARTHRITIS STRUCTURAL PROGRESSORS THROUGH THE USE OF SUPERVISED MACHINE LEARNING
Authors: H. Bonakdari, J. P. Pelletier, F. J. Blanco, I. Rego-Perez, A. Durán-Sotuela, D. Aitken, G. Jones, F. Cicuttini, A. Jamshidi, F. Abram, J. Martel-Pelletier
Journal: Annals of the Rheumatic Diseases (2022)

Background Knee osteoarthritis is the most prevalent chronic musculoskeletal debilitating disease. Current treatments are only symptomatic and to improve this, we need a robust prediction model to stratify patients at an early stage according to the risk of joint structure disease progression. Some genetic factors, including single nucleotide polymorphism (SNP) genes and mitochondrial (mt)DNA haplogroups/clusters, have been linked to this disease. Objectives For the first time, we aim to determine, by using machine learning, whether some SNP genes and mtDNA haplogroups/clusters alone or combined could predict early knee osteoarthritis structural progressors. Methods Participants (901) were first classified for the probability of being structural progressors. Genotyping included SNP genes TP63, FTO, GNL3, DUS4L, GDF5, SUPT3H, MCF2L, TGFA, mtDNA haplogroups H, J, T, Uk, others, and clusters HV, TJ, KU, C-others. They were considered for prediction with major risk factors of osteoarthritis, namely, age and body mass index (BMI). Seven supervised machine learning methodologies were evaluated. The support vector machine was used to generate gender-based models. The best input combination was assessed using sensitivity and synergy analyses. Validation was performed using 10-fold cross-validation as well as an external cohort (TASOAC). Results From 277 models, two were defined. Both used age and BMI in addition for the first one of the SNP genes TP63, DUS4L, GDF5, FTO with an accuracy of 85.0%; the second profits from the association of mtDNA haplogroups and SNP genes FTO and SUPT3H with 82.5% accuracy. The highest impact was associated with the haplogroup H, the presence of CT alleles for rs8044769 at FTO , and the absence of AA for rs10948172 at SUPT3H . Validation accuracy with the cross-validation (about 95%) and the external cohort (90.5%, 85.7%, respectively) was excellent for both models. Conclusion This study introduces a novel source of decision support in precision medicine in which, for the first time, two models were developed consisting of i) age, BMI, TP63, DUS4L, GDF5, FTO and ii) the optimum one as it has one less variable: age, BMI, mtDNA haplogroup, FTO, SUPT3H. Such a framework is translational and would be of benefit to patients at risk of structural progressive knee osteoarthritis. Acknowledgements The authors would like to thank the Osteoarthritis Initiative (OAI) participants and Coordinating Center for their work in generating the clinical and radiological data of the OAI cohort and for making them publicly available. The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public use data set and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners. None of the authors are part of the OAI investigator team. Moreover, the authors are also grateful to the TASOAC participants. A special thanks to ArthroLab Inc. for having provided the MRI data used for classifying structural progressors for each individual. Disclosure of Interests Hossein Bonakdari: None declared, Jean-Pierre Pelletier Shareholder of: ArthroLab Inc., Grant/research support from: Work supported in part by the Osteoarthritis Research Unit of the University of Montreal Hospital Research Centre and the Chair in Osteoarthritis from the University of Montreal., Francisco J. Blanco: None declared, Ignacio Rego-Perez: None declared, Alejandro Durán-Sotuela: None declared, Dawn Aitken: None declared, Graeme Jones: None declared, Flavia Cicuttini: None declared, Afshin Jamshidi Grant/research support from: Received a bursary from the Canada First Research Excellence Fund through the TransMedTech Institute in Canada., François Abram Employee of: was an employee of ArthroLab Inc., Johanne Martel-Pelletier Shareholder of: ArthroLab Inc., Grant/research support from: Work supported in part by the Osteoarthritis Research Unit of the University of Montreal Hospital Research Centre and the Chair in Osteoarthritis from the University of Montreal.